DRL-Based Joint Resource Allocation and Platoon Control Optimization for UAV-Hosted Platoon Digital Twin
Bibliographic record
Abstract
Digital twin (DT)-empowered platoon can improve platoon management efficiency and driving safety. However, the resource allocation scheme of low-latency platoon DT (PDT) and the interactions with platoon control strategy are important issues in the study of PDTs. In this article, we study the resource allocation in the PDT network and the interaction mechanism between PDT and platoon control for an unmanned aerial vehicle (UAV)-hosted PDT. We introduce the Age of Information (AoI) metrics to characterize the freshness of the DTs. To explore the impact of the PDT resource allocation scheme on the platoon control strategy, we propose a joint optimization model for power resource allocation and platoon control. Specifically, the allocation of power resources affects the PDT’s AoI, and the high-latency PDT in turn affects the platoon control strategy. Our objective is minimize the weighted sum of the system’s average energy consumption and the PDT’s average peak AoI. To solve the problem, we first reformulate the power resource allocation problem over a period of time as a Markov decision process (MDP) model, and then propose the Dirichlet deep deterministic policy gradient (DDPG)-based power allocation (D3PGPA) method based on Dirichlet distribution and DDPG algorithm. The method can not only effectively explores the state space while satisfying the constraints of limited resources but also improve the stability of the algorithm. Numerical results show that the D3PGPA method can host a PDT with low AoI and improve the stability of the platoon. Besides, our proposed method performs stably and outperforms other benchmark methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".